Github user mgaido91 commented on the issue:
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@cloud-fan I think this is just a wrong behavior (being against SQL
standard is not the only issue), so I am not sure that makes sense to have it
controlled by a config. Anyway, I agree that as it
Github user cloud-fan commented on the issue:
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I don't think we can change behavior like this without introducing a
config. In Spark 3.0 we will add a "strict mode" and closely follow SQL
standard. But for Spark 2.4, how about we keep the
Github user cloud-fan commented on the issue:
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ah that's a good point. Then maybe we should keep this "java style
behavior" and add "strict mode" later.
cc @rxin @rednaxelafx
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Github user mgaido91 commented on the issue:
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@cloud-fan ok, but then all arithmetic operations should be always
`nullable = true`. I am not sure whether this can introduce performance
regression (the additional checks are also going to
Github user cloud-fan commented on the issue:
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I prefer returning null, and introduce a strict mode in Spark 3.0. We can
revisit all the returning null cases and think if we should fail if strict mode
is on.
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kindly ping @cloud-fan @gatorsmile @hvanhovell
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@cloud-fan @gatorsmile @hvanhovell what do you think about deciding a
strategy about how to handle this case and try to make it happen in 2.4 as this
is a correctness bug? Thanks.
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kindly ping @gatorsmile @hvanhovell
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@cloud-fan it is not a regression, it has been like that at least since
2.0...
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is this a regression in Spark 2.3? which commit caused it?
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@cloud-fan @gatorsmile the main issue which is causing the UT failures,
now, is that since before we were allowing overflows, in aggregations we could
have an overflow eventually fixed by another
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